Please use this identifier to cite or link to this item: https://doi.org/10.1117/12.974443
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dc.titleA rooftop extraction method using color feature, height map information and road information
dc.contributor.authorXiang, Y.
dc.contributor.authorSun, Y.
dc.contributor.authorLi, C.
dc.date.accessioned2014-10-07T04:41:03Z
dc.date.available2014-10-07T04:41:03Z
dc.date.issued2012
dc.identifier.citationXiang, Y., Sun, Y., Li, C. (2012). A rooftop extraction method using color feature, height map information and road information. Proceedings of SPIE - The International Society for Optical Engineering 8537 : -. ScholarBank@NUS Repository. https://doi.org/10.1117/12.974443
dc.identifier.isbn9780819492777
dc.identifier.issn0277786X
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/83421
dc.description.abstractThis paper presents a new method for rooftop extraction that integrates color features, height map, and road information in a level set based segmentation framework. The proposed method consists of two steps: rooftop detection and rooftop segmentation. The first step requires the user to provide a few example rooftops from which the color distribution of rooftop pixels is estimated. For better robustness, we obtain superpixels of the input satellite image, and then classify each superpixel as rooftop or non-rooftop based on its color features. Using the height map, we can remove those detected rooftop candidates with small height values. Level set based segmentation of each detected rooftop is then performed based on color and height information, by incorporating a shape-prior term that allows the evolving contour to take on the desired rectangle shape. This requires performing rectangle fitting to the evolving contour, which can be guided by the road information to improve the fitting accuracy. The performance of the proposed method has been evaluated on a satellite image of 1 km×1 km in area, with a resolution of one meter per pixel. The method achieves detection rate of 88.0% and false alarm rate of 9.5%. The average Dice's coefficient over 433 detected rooftops is 73.4%. These results demonstrate that by integrating the height map in rooftop detection and by incorporating road information and rectangle fitting in a level set based segmentation framework, the proposed method provides an effective and useful tool for rooftop extraction from satellite images. © 2012 SPIE.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1117/12.974443
dc.sourceScopus
dc.subjectHeight map
dc.subjectLevel-set
dc.subjectRectangle fitting
dc.subjectRoad orientation
dc.subjectRoof segmentation
dc.subjectRooftop detection
dc.subjectSuperpixels
dc.typeConference Paper
dc.contributor.departmentELECTRICAL & COMPUTER ENGINEERING
dc.description.doi10.1117/12.974443
dc.description.sourcetitleProceedings of SPIE - The International Society for Optical Engineering
dc.description.volume8537
dc.description.page-
dc.description.codenPSISD
dc.identifier.isiut000316683600024
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